基于局部增强与区域拟合的活动轮廓模型

Active contour model based on local enhancement and region fitting

针对活动轮廓模型在分割弱边缘图像及严重的灰度不均匀图像方面存在轮廓曲线不能很好地演化到目标边界等问题,提出了一种基于局部增强与区域拟合的活动轮廓模型。首先,利用局部区域增强方法将原始图像转换为新图像,以增强图像的对比度。其次,利用统计信息计算图像的区域拟合能量。然后,加入正则项以避免演化轮廓重新初始化,提高图像分割效率。最后,通过灰度不均匀的合成图像和真实图像的实验,验证了该算法的有效性。

Aiming at the problem that the active contour model can’t evolve well to the target boundary in segmenting weak edge images and severe intensity inhomogeneous images,this paper proposed an active contour model based on local enhancement and region fitting.Firstly,it converted the original image to a new image by using a local area enhancement method to enhance the contrast of the image.Secondly,it used the statistical fit to calculate the region fitting energy of the image.Then,it added a regular term to avoid re-initialization of the evolution contour and improved image segmentation efficiency.Finally,it applied the model to synthetic and real images with intensity inhomogeneity,the experimental results validate the favorable performance of the proposed model.